The current trend in the management of mobile communication networks is to increase the level of automation in order to enhance network performance while reducing Operational Expenditure (OPEX). In this context, the 3rd Generation Partnership Project (3GPP) has presented different solutions. On the one hand, Self-Organizing Networks (SON) include self-healing capabilities, which allow operators to automate their troubleshooting tasks in order to identify and solve the problems of the network. On the other hand, the use of mobile traces or Minimization of Drive Tests (MDT) are proposed to automate the collection of user's measurements and signalling messages. This paper proposes to combine both solutions, SON and traces, with the purpose of quickly detecting and solving issues related to the radio interface. That is, the user information gathered by the cell traces function is used to perform an automatic diagnosis of the RF condition of each cell. In addition, the proposed approach allows to precisely locate RF problems based on the assessment of the RF condition. Mobile traces constitute large sets of data, whose analysis requires the application of big-data analytics techniques. The proposed system has been evaluated in two different live LTE networks, demonstrating its validity and utility.

Nowadays, the size and complexity of mobile networks are growing ceaselessly. Therefore, the management of mobile networks is a significant, expensive and demanding task to perform. In order to simplify this task, Self-Organizing Networks (SON) appear as a unified solution to autonomously manage a mobile network. One of the fundamental functions of SON is self-healing. Within self- healing, the objective of fault diagnosis or root cause analysis is the identification of problem causes in faulty cells. With that aim, in this paper, an unsupervised diagnosis system for LTE (Long Term Evolution) based on Bayesian networks is presented. In particular, the system is divided in two separate steps. First of all, the discretization of the input data is done. Then, the system provides an identification of the cell status. Depending on the discretization method, the performance of the system is different, so, in this paper, different methods have been evaluated. Results have proven the high success rate achieved with the proposed system, particularly when the Expectation-Maximization (EM) algorithm is used for the discretization.

The recent developments in cellular networks, along with the increase in services, users and the demand of high quality have raised the Operational Expenditure (OPEX). Self-Organizing Networks (SON) are the solution to reduce these costs. Within SON, self-healing is the functionality that aims to automatically solve problems in the radio access network, at the same time reducing the downtime and the impact on the user experience. Self-healing comprises four main functions: fault detection, root cause analysis, fault compensation and recovery. To perform the root cause analysis (also known as diagnosis), Knowledge-Based Systems (KBS) are commonly used, such as fuzzy logic. In this paper, a novel method for extracting the Knowledge Base for a KBS from solved troubleshooting cases is proposed. This method is based on data mining techniques as opposed to the manual techniques currently used. The data mining problem of extracting knowledge out of LTE troubleshooting information can be considered a Big Data problem. Therefore, the proposed method has been designed so it can be easily scaled up to process a large volume of data with relatively low resources, as opposed to other existing algorithms. Tests show the feasibility and good results obtained by the diagnosis system created by the proposed methodology in LTE networks.